Flies and smells
Connectomics informed modelling of olfactory learning in Drosophila Melanogaster
Credit assignment
- Deep neural networks need to set their weights effectively to function
- Backpropagation is infeasible in biological networks
- How networks assign credit remains a major area of research in neuroscience
- Solutions consist of a combination of synaptic learning rules and architectural motifs
Dopaminergic error signals
- Modulatory signals affect plasticity at the synapse
- 3-factor learning allows weights to change in an error-dependent manner
- Dopaminergic neurons are traditionally thought to convey global error signals
- Zero-order optimization is inefficient in large networks
- Research direction: study the connectivity of dopaminergic neurons in a learning centre
Fruit fly associative learning
- Drosophila excel at classical (Pavlovian) conditioning, associating stimuli like odors with rewards or punishments
- Learning is rapid and efficient, often requiring only a few trials
- This provides an excellent minimal model of learning
The advent of connectomics
- Researchers have recently constructed a complete, synaptic-resolution wiring diagram of the adult Drosophila brain (Schlegel et al. 2024)
- Heroic work, using electron-microscopy, machine vision, and extensive curation
- Allows us to study the learning circuitry of Drosophila in detail
Olfactory processing circuits
- Odors are first detected by olfactory receptor neurons (ORNs) and processed in the Antennal Lobe (AL)
- Projection Neurons (PNs) relay information to the Mushroom Body (MB), a key center for learning and memory
- Kenyon Cells (KCs) in the MB sparsely encode odors, enhancing pattern separation
- Dopaminergic neurons modulate synapses in response to reward or punishment, reinforcing associative learning
- MB output neurons (MBONs) receive inputs from Kenyon Cells, and drive either attractive or aversive behaviours
Compartmentalised mushroom body structure
- 15 compartments with distinct MBONs representing positive or negative valences
- Kenyon cells project within a given compartment with little overlap
- Dopaminergic neurons send error signals within their respective compartments
- Different lobes known to have different learning rates, and are implicated in associative memory across different timescales
- The functional role of this structure remains unknown
Research question:
Why is the mushroom body compartmentalised?
- How do the following factors affect learning:
- The number of compartments
- The distribution of compartment valences
- The distribution of compartment learning rates
The model:
- A simplified ensemble of perceptrons
- Output logits representing confidence in their respective valences
- Trained individually with their own error signals
- Compartment signals integrated to produce a global valence prediction
The task:
- Input random binary vectors with biologically accurate dimensionality and sparsity
- Train to predict valence scores of 1 or -1 associated with each pattern
Comparison of single- vs multi-compartments
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Dynamically changing targets
Dynamically changing targets
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Multiple rates of changing targets
Conclusions
- Having a number of compartments seems to help learning somewhat
- An ensemble of perceptrons will find an optimal learning rate for an environment with a given rate of variability in valence assignments
- Couldn’t get a model to work with a variety of different rates of changing valences
Future work
- The success of the ensemble model needs further theoretical and computational exploration
- Need to find a model that works on data with a non-constant rate of change